Gesture recognition has gained in popularity in a wide range of applications, from user interfaces (UIs) to enhanced computer animation. One common technique to implement gesture recognition relies on supervised machine learning for classifying and identifying gestures performed by a user. However, successful application of this technique requires consideration of several issues.
An adequate training set must be built to produce a reliable classification function. Noise and distortion of the detected gesture and of the underlying training set may inhibit reliable identification and classification.
Hand gestures, with twenty nine degrees of motion are particularly challenging to classify. A machine learning process for such a complex tasks typically requires a large amount of training data where to faithfully represent the camera noise, and hand-finger locations and orientations that would be observed in a non-controlled scenario.
Typical techniques to acquire training data use visual markers such as painted gloves, stickers, reflectors or LEDs attached to different parts of the hand or a hand glove for observation by one or more cameras. The positions and orientations of the hand and fingers may then extracted from this visually tracked data. However, visually captured data suffers from occluded markers due to the dimensions and articulation possibilities of the human hand, resulting in incomplete data. Additionally the visual markers themselves may distort the true hand profile and/or impede natural hand motion, compromising the training data.
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